A Plug&Play P300 BCI Using Information Geometry

Abstract--This paper presents a new classification methods for Event Related Potentials (ERP) based on an Information geometry framework. Through a new estimation of covariance matrices, this work extend the use of Riemannian geometry, which was previously limited to SMR-based BCI, to the problem of classification of ERPs. As compared to the state-of-the-art, this new method increases performance, reduces the number of data needed for the calibration and features good generalisation across sessions and subjects. This method is illustrated on data recorded with the P300-based game brain invaders. Finally, an online and adaptive implementation is described, where the BCI is initialized with generic parameters derived from a database and continiously adapt to the individual, allowing the user to play the game without any calibration while keeping a high accuracy. So far we have conceived a Brain-Computer Interface (BCI) as a learning machine where the classifier is trained in a calibration phase preceding immediately the actual BCI use [1]. Depending on the BCI paradigm and on the efficiency of the classifier, the calibration phase may last from a few to several minutes. Regardless the duration, the very necessity of a calibration session reduces drastically the usability and appealing of a BCI. This is true both for clinically-oriented BCI, where the cognitive skills of patients are often limited and are wasted in the calibration phase, and for healthy users where the plug&play operation is nowadays considered as a minimum requirement for any consumer interfaces and devices. Besides the essential considerations from the user perspective, it appears evident that training the BCI at the beginning of each session and discarding the calibration data at the end is a very inefficient way to proceed. The problem we pose here is: can we design a "plug&play" BCI? Of course, such a goal does not imply that the BCI is not calibrated.